library(flexdashboard)
library(readr)
library(lubridate)
library(shiny)
library(jsonlite)
library(maptools)
library(ggplot2)
library(tidyr)
library(dplyr)
library(purrr)
library(leaflet)
library(plotly)
library(DT)
library(ggthemes)
library(viridis)
library(tidyverse)
library(tigris)


nyc_squirrels <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-29/nyc_squirrels.csv")

nyc_squirrels_clean <- nyc_squirrels %>% 
  select(long, lat, unique_squirrel_id, hectare, date, hectare_squirrel_number,
         age, primary_fur_color, highlight_fur_color, running, chasing,
         climbing, eating, foraging, kuks, quaas, moans, tail_flags,
         tail_twitches, indifferent, runs_from, community_districts,
         police_precincts) %>% 
  mutate(date = mdy(date)) %>% 
  na.omit()
central_park <- metro_divisions(class = "sf") %>% 
  filter(NAME == "New York-Jersey City-White Plains, NY-NJ")

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# leaflet map for central park
 leaflet() %>% 
  addTiles() %>% 
  fitBounds(-73.9823592,40.7636484,-73.9492441, 40.800553)
## Table 1

primary_fur_color <- nyc_squirrels_clean %>%
  group_by(primary_fur_color) %>% 
  summarize(count = n())

df <- nyc_squirrels_clean %>%
  group_by(running) %>% 
  summarize(count = n())